By Gustavo M. de Athayde, Renato G. Flôres Jr. (auth.), Christian L. Dunis (eds.)
Advances in Quantitative Asset Management comprises chosen articles which, for the main half, have been awarded on the `Forecasting monetary Markets' convention. `Forecasting monetary Markets' is a world convention on quantitative finance that's held in London in may well each year. due to the fact its inception in 1994, the convention has grown in scope and stature to develop into a key overseas assembly aspect for these attracted to quantitative finance, with the participation of prestigious educational and study associations from around the world, together with significant crucial banks and quantitative fund managers.
The editor has selected to be aware of advances in quantitative asset administration and, as a result, the papers during this ebook are geared up round significant issues: advances in asset allocation and portfolio administration, and modelling threat, go back and correlation.
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Advances in Quantitative Asset administration includes chosen articles which, for the main half, have been awarded on the `Forecasting monetary Markets' convention. `Forecasting monetary Markets' is a world convention on quantitative finance that's held in London in could each year. because its inception in 1994, the convention has grown in scope and stature to develop into a key foreign assembly element for these attracted to quantitative finance, with the participation of prestigious educational and learn associations from world wide, together with significant valuable banks and quantitative fund managers.
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Extra info for Advances in Quantitative Asset Management
CONCLUSION This paper is a formal presentation of the multivariate Student model for returns on financial assets. The paper shows that, under this model, portfolio returns also follow a Student t distribution. There is also a Student version of the market model. It preserves the linear regression type relationship between returns on stocks and returns on the market. However, the stock specific volatilities are time varying. The time variation is attributable to the market return. For users of quadratic utility, there are no practical obstacles to using the model for portfolio selection.
The parameter (i), which must be positive, is known as the degrees of freedom. It controls the extent of the fat tails: the smaller (i), the fatter the tails. Conversely, as 00 increases the distribution approaches the multivariate normal. The matrix V is symmetric (without loss of generality) and is positive defmite. Some examples of the Student t density are shown in figure 1. These are for 1, 20 and 00 degrees of freedom and illustrate the fatness of the tails for 00 = 1,20 when compared with normality (00 = 00).
While it is always true that the expected value of the error term Ei must be zero if the CAPM itself is true, it is not necessarily the case that this model will satisfy the statistical assumptions of regression that are a necessary prerequisite to estimating betas using ordinary least squares (OLS). To use OLS it is necessary that the error terms are normally distributed and are independent of market return. It is well known that departures from these assumptions can seriously reduce the accuracy of estimates computed using OLS.